Canadian spontaneous coronary artery dissection cohort study: in-hospital and 30-day outcomes
Bibliographic record
Abstract
AIMS: Spontaneous coronary artery dissection (SCAD) was underdiagnosed and poorly understood for decades. It is increasingly recognized as an important cause of myocardial infarction (MI) in women. We aimed to assess the natural history of SCAD, which has not been adequately explored. METHODS AND RESULTS: We performed a multicentre, prospective, observational study of patients with non-atherosclerotic SCAD presenting acutely from 22 centres in North America. Institutional ethics approval and patient consents were obtained. We recorded baseline demographics, in-hospital characteristics, precipitating/predisposing conditions, angiographic features (assessed by core laboratory), in-hospital major adverse events (MAE), and 30-day major adverse cardiovascular events (MACE). We prospectively enrolled 750 SCAD patients from June 2014 to June 2018. Mean age was 51.8 ± 10.2 years, 88.5% were women (55.0% postmenopausal), 87.7% were Caucasian, and 33.9% had no cardiac risk factors. Emotional stress was reported in 50.3%, and physical stress in 28.9% (9.8% lifting >50 pounds). Predisposing conditions included fibromuscular dysplasia 31.1% (45.2% had no/incomplete screening), systemic inflammatory diseases 4.7%, peripartum 4.5%, and connective tissue disorders 3.6%. Most were treated conservatively (84.3%), but 14.1% underwent percutaneous coronary intervention and 0.7% coronary artery bypass surgery. In-hospital composite MAE was 8.8%; peripartum SCAD patients had higher in-hospital MAE (20.6% vs. 8.2%, P = 0.023). Overall 30-day MACE was 8.8%. Peripartum SCAD and connective tissue disease were independent predictors of 30-day MACE. CONCLUSION: Spontaneous coronary artery dissection predominantly affects women and presents with MI. Despite majority of patients being treated conservatively, survival was good. However, significant cardiovascular complications occurred within 30 days. Long-term follow-up and further investigations on management are warranted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".